neuro_dataset.py 文件源码

def make_td_probability_image(td, skip_steps=0, is_normalize = False):
    """Generate image from the Temporal Difference (td) events with each pixel value indicating probability of a spike within a 1 millisecond time step. 0 = 0%. 255 = 100%
    td is read from a binary file (refer to eventvision.Readxxx functions)
    td: eventvision.Events
    skip_steps: number of time steps to skip (to allow tracker to init to a more correct position)
    is_normalize: True to make the images more obvious (by scaling max probability to pixel value 255)
    """
    assert isinstance(td, ev.Events)

    #with timer.Timer() as my_timer:
    event_offset = 0
    combined_image = np.zeros((td.height, td.width), np.float32)
    offset_ts = td.data[0].ts + (skip_steps * 1000)
    num_time_steps = math.floor((td.data[-1].ts - offset_ts) / 1000)

    current_frame = np.zeros((td.height, td.width), np.uint8)
    for start_ts in range(int(offset_ts), td.data[-1].ts, 1000):
        end_ts = start_ts + 1000
        frame_data = td.data[(td.data.ts >= start_ts) & (td.data.ts < end_ts)]
        current_frame.fill(0)
        current_frame[frame_data.y, frame_data.x] = 1 
        combined_image = combined_image + current_frame        

    #print 'Making image out of bin file took %s seconds' % my_timer.secs
    if (is_normalize):
        combined_image = (combined_image / np.max(combined_image))
    else:
        combined_image = (combined_image / num_time_steps)

    return combined_image